“The awakening of every single country is that they have to control their AI intelligence and not outsource their data" - Jensen Huang. Sovereign AI is rapidly becoming a fundamental national utility, much like defense, energy or telecoms. Nations worldwide recognize that AI sovereignty—having control over your AI infrastructure, data, and models—is essential for economic progress, security, and especially independence - especially when the US is pushing protectionism and trying to prevent global AI innovation. Of course this has the opposite effect - DeepSeek created by a Hedge Fund in China; India building the world's largest AI data centre (3 GW), and global software teams scaling, learning and building faster than ever before. However most countries lack the talent, financing and experience to implement Sovereign AI for their requirements - and it is our belief at Frontier One, that one of the biggest markets for AI applications, cloud services and GPUs will be global governments. We see it already - with $10B of GPUs in 2024 bought directly by governments - and it's rapidly expanding. We will talk about what Sovereign AI is - both infrastructure and software details / why it is crucial for a nation / how to get involved as part of the MLOps community.

No YAML? No Problem: Orchestrate Kubernetes Workflows the Easy Way with Python Sick of writing orchestration logic in YAML? You’re not alone. Discover how Hera, the Python SDK for Argo Workflows, lets you express complex Kubernetes workflows using clean, testable Python code. Keep your business logic and orchestration logic in one place — no indentation nightmares required. Building Reliable AI Applications with Durable Workflows Chaining functions together is easy. Keeping AI workflows running when things go sideways? That’s the hard part. This talk introduces durable workflows — systems that checkpoint state, recover automatically, and gracefully handle everything from human delays to API flakiness. You’ll see real examples of AI pipelines that stay resilient in production. Process Calling: Agentic Tools Need State Function calling gave LLMs a way to "do" things — but it’s not enough. When you’re building agents for customer-facing use cases, stateless abstractions fall short fast. Learn why the future of agentic tooling is process-based, not function-based, and what it means to build agents that remember, recover, and reliably finish what they start.

Anthropic's Model Context Protocol (MCP) is revolutionizing AI integration by providing a standardized way for LLMs to connect with external systems. Learn the core concepts, architecture, and build a practical Hacker News server that extends Claude's capabilities with real-time data access.

Kai Wang joins the MLOps Community podcast LIVE to share how Uber built and scaled its ML platform, Michelangelo. From mission-critical models to tools for both beginners and experts, he walks us through Uber’s AI playbook—and teases plans to open-source parts of it.

The slow, batch-processing nature of the data lake is obsolete for modern Generative AI, which requires instant access to fresh data. In this article, the author proposes a shift away from centralizing data, advocating instead for an API-first approach. This allows AI applications to directly and quickly access live data from its source, enabling truly real-time, responsive features.

LLMs are reshaping the future of data and AI—and ignoring them might just be career malpractice. Yoni Michael and Kostas Pardalis unpack what’s breaking, what’s emerging, and why inference is becoming the new heartbeat of the data pipeline.

What makes a good AI benchmark? Greg Kamradt joins Demetrios to break it down—from human-easy, AI-hard puzzles to wild new games that test how fast models can truly learn. They talk hidden datasets, compute tradeoffs, and why benchmarks might be our best bet for tracking progress toward AGI. It’s nerdy, strategic, and surprisingly philosophical.

I’m sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company’s operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard’s intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work.